Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/114504
Title: A landscape of pharmacogenomic interactions in cancer
Author: Iorio, Francesco
Knijnenburg, Theo A.
Vis, Daniel J.
Bignell, Graham R.
Menden, Michael P.
Schubert, Michael
Aben, Nanne
Gonçalves, Emanuel
Barthorpe, Syd
Lightfoot, Howard
Cokelae, Thomas
Greninger, Patricia
Dyk, Ewald van
Chang, Han
Silva, Heshani de
Heyn, Holger
Deng, Xianming
Egan, Regina K.
Liu, Qingsong
Mironenko, Tatiana
Mitropoulos, Xeni
Richardson, Laura
Wang, Jinhua
Zhang, Tinghu
Moran, Sebastian
Sayols, Sergi
Soleimani, Maryam
Tamborero, David
López Bigas, Núria
Ross-Macdonald, Petra
Esteller, Manel
Gray, Nathanael S.
Haber, Daniel A.
Stratton, Michael R.
Benes, Cyril H.
Wessels, Lodewyk F.A.
Saez-Rodriguez, Julia
McDermott, Ultan
Garnett, Mathew J.
Keywords: Càncer
Oncogènesi
Medicaments antineoplàstics
Resistència als medicaments
Farmacogenètica
Genomes
Fenotip
Cancer
Carcinogenesis
Antineoplastic agents
Drug resistance
Pharmacogenetics
Genomes
Phenotype
Issue Date: 28-Jul-2016
Publisher: Cell Press
Abstract: Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.
Note: Reproducció del document publicat a: https://doi.org/10.1016/j.cell.2016.06.017
It is part of: Cell, 2016, vol. 166, num. 3, p. 740-754
Related resource: https://doi.org/10.1016/j.cell.2016.06.017
URI: http://hdl.handle.net/2445/114504
ISSN: 0092-8674
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Articles publicats en revistes (Ciències Fisiològiques)

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